The cause of residual low back pain in patients with osteoporotic vertebral compression fractures(OVCFs) after PKP remains highly controversial, and our goal was toinvestigate the most likely cause and to develop a novel nomogram for the prediction ofresidual low back pain and to evaluate the predictive performance of the model. The clinical data of 281 patients with OVCFs who underwent PKP at our hospital from July 2019 toJuly 2020 were reviewed. The optimal logistic regression model was determined by lassoregression for multivariate analysis, thus constructing a nomogram. Bootstrap was used toperfomance the internal validation; receiver operating characteristic (ROC) curve, calibration curve, anddecision curve analysis (DCA) were used to assess the predictive performance and clinical utility of themodel, respectively. Temporal external validation of the model was also performed using retrospectivedata from 126 patients who underwent PKP at our hospital from January 2021 to October 2021. Lasso regression cross-validation showed that the variables with non-zero coefficients were thenumber of surgical vertebrae, preoperative bone mineral density (pre-BMD), smoking history,thoracolumbar fascia injury (TLFI), intraoperative facet joint injury (FJI), and postoperative incompletecementing of the fracture line (ICFL). The above factors were included in the multivariate analysis andshowed that the pre-BMD, smoking history, TLFI, FJI, and ICFL were independent risk factors forresidual low back pain (P < 0.05). The ROC and calibration curve of the original model and temporalexternal validation indicated a good predictive power of the model. The DCA curve suggested that themodel has good clinical practicability. The risk prediction model has good predictive performance and clinical practicability, which canprovide a certain basis for clinical decision-making in patients with OVCFs.